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Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

The rapid development of e-commerce has caused not only explosive growth of the express delivery industry, but also ever-greater operational pressures. Models from the sharing economy may provide new ideas for operational improvement. The purpose of this paper is to consider an optimization method that reduces costs and increases efficiency. The proposed method enables a shared distribution system based on revenue-sharing and cooperative investment contracts.

The authors design a two-echelon supply chain (SC) of the shared distribution system with one shared distribution company and N express companies. In this SC, the express companies provide only inter-city transportation, and they outsource internal-city transportation to a shared distribution company. This distribution system differs from that of the traditional express delivery industry. The traditional system of delivery requires large numbers of empty trips (with no load to deliver), because the operating mode of urban distribution has been the franchise. To offer greater efficiency and performance, the authors introduce the sharing economy mode of express delivery. The authors examine the potential of a joint optimal decision-making strategy that involves revenue-sharing and cooperative investment contracts based on an order flow proportion (OFP) and a revenue-sharing factor (RSF). In this shared distribution system, the most important innovation is that all of the express companies jointly invest in and establish a shared distribution company based on OFP or RSF principles.

The profitability of an SC with revenue-sharing contracts based on an OFP system is much higher than that of a decentralized SC, and it is very close to the profitability of a centralized SC. In SCs with revenue-sharing contracts that are based on RSFs, there are many possible combinations of RSFs that can increase the overall profitability. The analyses indicate that the OFP system offers the best solution in designing revenue-sharing contracts based on RSFs.

This study indicates that revenue-sharing contracts based on both OFP and RSF principles can increase overall SC returns by 0.21 to 0.44 percent. In sum total, this improvement could mean a 0.84 to 1.76bn Yuan increase in revenues for the 400+ bn-Yuan express delivery industry.

The authors find that a combination of equity investment and SC coordination contracts makes the cooperation between SC members much more stable. Through this kind of shared distribution system, the scale of economy can further reduce the costs and increase the efficiency of the express delivery industry.

This paper aims to analyze high-quality papers on the research of electronic word-of-mouth (eWOM) for product and service quality improvement from 2009 to 2022, in order to fully understand their historical progress, current situation and future development trend.

This paper adopts the bibliometrics method to analyze the relevant literature, including publishing trend and citation status, regional and discipline area distribution, and influential publications. Secondly, the VOSviewer is used for literature co-citation analysis and keyword co-occurrence analysis to obtain the basic literature and research hotspots in this research field.

Firstly, the study finds that the number of publications basically shows an increasing trend, and those publications are mainly published in tourism journals. In addition, among these papers, China has the largest number of publications, followed by the USA and South Korea. Through co-citation analysis of literature and keyword co-occurrence analysis, 22 foundational papers and six main research topics are obtained in this paper. Finally, this paper elaborates on the development trend of the research topic and future research directions in detail.

This is the first paper that uses bibliometrics to analyze and review relevant researches on eWOM for product and service quality improvement, which is helpful for researchers to quickly understand its development status and trend. This review also provides some future research directions and provides a reference for further research.